A novel
image-based respiratory self-navigation method was developed for
free-breathing coronary MRI. Under-sampled radial sub-images are acquired on
a beat-to-beat basis, non-linear reconstruction is performed, and motion
parameters are extracted for direct motion correction. In a first step, the
new algorithm was optimized and evaluated using a numerical simulation of a
moving heart. Subsequently, the performance was quantitatively ascertained in
an in vivo study that included 12 healthy adult subjects where it was
objectively demonstrated that self-navigation incorporating compressed
sensing is a powerful tool for motion artifact suppression in radial coronary
MRI.